{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PrithviWxC\n",
    "\n",
    "This notebook will walk you through how to construct the model,\n",
    "load the weights, build the dataset, and use the model for inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "from huggingface_hub import hf_hub_download, snapshot_download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now configure the backends and torch states, including setting the seeds for the RNGs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: NVIDIA A100-SXM4-80GB\n"
     ]
    }
   ],
   "source": [
    "torch.jit.enable_onednn_fusion(True)\n",
    "if torch.cuda.is_available():\n",
    "    print(f\"Using device: {torch.cuda.get_device_name()}\")\n",
    "    torch.backends.cudnn.benchmark = True\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "random.seed(42)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed(42)\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has approximately 2.3 billion parameters, so it\n",
    "requires reasonable computational resources, but it is possible\n",
    "to run it on a CPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader\n",
    "### Variables and times\n",
    "\n",
    "With the environment ready to go, we now need to set up the task.\n",
    "The core model expects a fixed set of variables from the MERRA-2\n",
    "dataset, which are prescribed below. The variables are comprised\n",
    "of surface variables, surface static variables, and variables at\n",
    "various vertical levels within the atmosphere. More details on the\n",
    "MERRA-2 dataset can be found\n",
    "[here](https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/).\n",
    "\n",
    "The MERRA-2 dataset includes data at longitudes of $-180^\\circ$\n",
    "and $+180^\\circ$. This represents duplicate data, so we set a\n",
    "padding variable to remove it.\n",
    "\n",
    "The input to the core model consists of these variables at two\n",
    "different times. The time difference in hours between these samples\n",
    "is passed to the model and set in the input_time variable.\n",
    "\n",
    "The model's task is to predict the fixed set of variables at a\n",
    "target time, given the input data.\n",
    "\n",
    "For example, if the input times are 0900 and 1200, resulting in\n",
    "an input_time of -3, then a lead_time of 6 would result in a\n",
    "target time of 1800."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "surface_vars = [\n",
    "    \"EFLUX\",\n",
    "    \"GWETROOT\",\n",
    "    \"HFLUX\",\n",
    "    \"LAI\",\n",
    "    \"LWGAB\",\n",
    "    \"LWGEM\",\n",
    "    \"LWTUP\",\n",
    "    \"PS\",\n",
    "    \"QV2M\",\n",
    "    \"SLP\",\n",
    "    \"SWGNT\",\n",
    "    \"SWTNT\",\n",
    "    \"T2M\",\n",
    "    \"TQI\",\n",
    "    \"TQL\",\n",
    "    \"TQV\",\n",
    "    \"TS\",\n",
    "    \"U10M\",\n",
    "    \"V10M\",\n",
    "    \"Z0M\",\n",
    "]\n",
    "static_surface_vars = [\"FRACI\", \"FRLAND\", \"FROCEAN\", \"PHIS\"]\n",
    "vertical_vars = [\"CLOUD\", \"H\", \"OMEGA\", \"PL\", \"QI\", \"QL\", \"QV\", \"T\", \"U\", \"V\"]\n",
    "levels = [\n",
    "    34.0,\n",
    "    39.0,\n",
    "    41.0,\n",
    "    43.0,\n",
    "    44.0,\n",
    "    45.0,\n",
    "    48.0,\n",
    "    51.0,\n",
    "    53.0,\n",
    "    56.0,\n",
    "    63.0,\n",
    "    68.0,\n",
    "    71.0,\n",
    "    72.0,\n",
    "]\n",
    "padding = {\"level\": [0, 0], \"lat\": [0, -1], \"lon\": [0, 0]}\n",
    "\n",
    "lead_times = [6]  # This varibale can be change to change the task\n",
    "input_times = [-6]  # This varibale can be change to change the task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data file\n",
    "MERRA-2 data is available from 1980 to the present day,\n",
    "at 3-hour temporal resolution. The dataloader we have provided\n",
    "expects the surface data and vertical data to be saved in\n",
    "separate files, and when provided with the directories, will\n",
    "search for the relevant data that falls within the provided time range.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_range = (\"2020-01-01T00:00:00\", \"2020-01-01T23:59:59\")\n",
    "\n",
    "surf_dir = Path(\"./merra-2\")\n",
    "snapshot_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    allow_patterns=\"merra-2/MERRA2_sfc_2020010[1].nc\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "vert_dir = Path(\"./merra-2\")\n",
    "snapshot_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    allow_patterns=\"merra-2/MERRA_pres_2020010[1].nc\",\n",
    "    local_dir=\".\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Climatology\n",
    "The PrithviWxC model was trained to calculate the output by\n",
    "producing a perturbation to the climatology at the target time.\n",
    " This mode of operation is set via the `residual=climate` option.\n",
    " This was chosen as climatology is typically a strong prior for\n",
    " long-range prediction. When using the `residual=climate` option,\n",
    " we have to provide the dataloader with the path of the\n",
    " climatology data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "surf_clim_dir = Path(\"./climatology\")\n",
    "snapshot_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    allow_patterns=\"climatology/climate_surface_doy00[1]*.nc\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "vert_clim_dir = Path(\"./climatology\")\n",
    "snapshot_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    allow_patterns=\"climatology/climate_vertical_doy00[1]*.nc\",\n",
    "    local_dir=\".\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Postion encoding\n",
    "Position data is included in the data passed to the model,\n",
    "as this allows the attention mechanism to determine data\n",
    "locality rather than explicit or implicit data connections.\n",
    "The position data is encoded in the model with two possible\n",
    "options, fourier or absolute. As these encoding options\n",
    "require different treatment within the data loader, the\n",
    "chosen option is set here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "positional_encoding = \"fourier\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset init\n",
    "We can now instantiate the MERRA2 Dataset class provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PrithviWxC.dataloaders.merra2 import Merra2Dataset\n",
    "\n",
    "dataset = Merra2Dataset(\n",
    "    time_range=time_range,\n",
    "    lead_times=lead_times,\n",
    "    input_times=input_times,\n",
    "    data_path_surface=surf_dir,\n",
    "    data_path_vertical=vert_dir,\n",
    "    climatology_path_surface=surf_clim_dir,\n",
    "    climatology_path_vertical=vert_clim_dir,\n",
    "    surface_vars=surface_vars,\n",
    "    static_surface_vars=static_surface_vars,\n",
    "    vertical_vars=vertical_vars,\n",
    "    levels=levels,\n",
    "    positional_encoding=positional_encoding,\n",
    ")\n",
    "assert len(dataset) > 0, \"There doesn't seem to be any valid data.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The model\n",
    "We are now ready to build the mdoel.\n",
    "### Scalers\n",
    "Additionally, the model takes as static parameters the mean\n",
    "and variance values of the input variables and the variance\n",
    "values of the target difference, i.e., the variance between\n",
    "climatology and instantaneous variables. We have provided\n",
    "data files containing these values, and here we load this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PrithviWxC.dataloaders.merra2 import (\n",
    "    input_scalers,\n",
    "    output_scalers,\n",
    "    static_input_scalers,\n",
    ")\n",
    "\n",
    "surf_in_scal_path = Path(\"./climatology/musigma_surface.nc\")\n",
    "hf_hub_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    filename=f\"climatology/{surf_in_scal_path.name}\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "vert_in_scal_path = Path(\"./climatology/musigma_vertical.nc\")\n",
    "hf_hub_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    filename=f\"climatology/{vert_in_scal_path.name}\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "surf_out_scal_path = Path(\"./climatology/anomaly_variance_surface.nc\")\n",
    "hf_hub_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    filename=f\"climatology/{surf_out_scal_path.name}\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "vert_out_scal_path = Path(\"./climatology/anomaly_variance_vertical.nc\")\n",
    "hf_hub_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    filename=f\"climatology/{vert_out_scal_path.name}\",\n",
    "    local_dir=\".\",\n",
    ")\n",
    "\n",
    "in_mu, in_sig = input_scalers(\n",
    "    surface_vars,\n",
    "    vertical_vars,\n",
    "    levels,\n",
    "    surf_in_scal_path,\n",
    "    vert_in_scal_path,\n",
    ")\n",
    "\n",
    "output_sig = output_scalers(\n",
    "    surface_vars,\n",
    "    vertical_vars,\n",
    "    levels,\n",
    "    surf_out_scal_path,\n",
    "    vert_out_scal_path,\n",
    ")\n",
    "\n",
    "static_mu, static_sig = static_input_scalers(\n",
    "    surf_in_scal_path,\n",
    "    static_surface_vars,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task and additional configs\n",
    "As previously mentioned, the PrithviWxC model's pretext task\n",
    "involved predicting the desired variable at a specific lead\n",
    "time. This was achieved by calculating the difference (delta)\n",
    "compared to the climatological average at that time. This\n",
    "operational mode is activated using the residual flag. Although\n",
    "the model includes additional residual options, the core model\n",
    "weights were not trained using these modes.\n",
    "\n",
    "Additionally, for training and evaluation, it is possible to\n",
    "mask tokens in the model. The masking occurs after tokenization,\n",
    "prior to the encoder layers. The model utilizes multi-axis\n",
    "attention, with data broken down into a hierarchy of local and\n",
    "global patches. Consequently, masking can be configured to mask\n",
    "either small local patches or larger global patches. This\n",
    "configuration is achieved via the `masking_mode` flag. It is\n",
    "possible to set `masking_mode=both`. This does not mix the modes\n",
    "but rather allows both modes to be used and swapped between,\n",
    "primarily for training purposes. For this demonstration, we will\n",
    "adjust the masking ratio to showcase the reconstruction\n",
    "capabilities of the model.\n",
    "\n",
    "Finally, we can set up shifting. Primarily utilized in the\n",
    "decoder, this enables alternate shifting of the attention\n",
    "windows, similar to the SWIN model. This option necessitates\n",
    "an even number of decoder blocks and is incompatible with the\n",
    "encoder when masking is also employed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "residual = \"climate\"\n",
    "masking_mode = \"local\"\n",
    "decoder_shifting = True\n",
    "masking_ratio = 0.99"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model init\n",
    "We now have all the pieces to build the model. If you are\n",
    "using the pretrained weights, a number of the model\n",
    "hyperparameters are predetermined and included below. With\n",
    "this configuration, the model will have approximately 2.3\n",
    "billion parameters. Therefore, if you want to train the fully\n",
    "unfrozen model, you will likely need to use a model distribution\n",
    "approach, such as fully shared data parallelism (FSDP). To\n",
    "further reduce the memory usage of the model when gradients are\n",
    "required, there are two variables — `checkpoint_encoder` and\n",
    "`checkpoint_decoder` — which enable activation checkpointing of\n",
    "desired transformer layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PrithviWxC.model import PrithviWxC\n",
    "\n",
    "model = PrithviWxC(\n",
    "    in_channels=160,\n",
    "    input_size_time=2,\n",
    "    in_channels_static=8,\n",
    "    input_scalers_mu=in_mu,\n",
    "    input_scalers_sigma=in_sig,\n",
    "    input_scalers_epsilon=0.0,\n",
    "    static_input_scalers_mu=static_mu,\n",
    "    static_input_scalers_sigma=static_sig,\n",
    "    static_input_scalers_epsilon=0.0,\n",
    "    output_scalers=output_sig**0.5,\n",
    "    n_lats_px=360,\n",
    "    n_lons_px=576,\n",
    "    patch_size_px=[2, 2],\n",
    "    mask_unit_size_px=[30, 32],\n",
    "    mask_ratio_inputs=masking_ratio,\n",
    "    embed_dim=2560,\n",
    "    n_blocks_encoder=12,\n",
    "    n_blocks_decoder=2,\n",
    "    mlp_multiplier=4,\n",
    "    n_heads=16,\n",
    "    dropout=0.0,\n",
    "    drop_path=0.0,\n",
    "    parameter_dropout=0.0,\n",
    "    residual=residual,\n",
    "    masking_mode=masking_mode,\n",
    "    decoder_shifting=decoder_shifting,\n",
    "    positional_encoding=positional_encoding,\n",
    "    checkpoint_encoder=[],\n",
    "    checkpoint_decoder=[],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load weights\n",
    "We have provided unshared pretrained weights for the model,\n",
    "which can now be loaded. The model can then be transferred\n",
    "to the requested device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_path = Path(\"./weights/prithvi.wxc.2300m.v1.pt\")\n",
    "hf_hub_download(\n",
    "    repo_id=\"Prithvi-WxC/prithvi.wxc.2300m.v1\",\n",
    "    filename=weights_path.name,\n",
    "    local_dir=\"./weights\",\n",
    ")\n",
    "\n",
    "state_dict = torch.load(weights_path, weights_only=False)\n",
    "if \"model_state\" in state_dict:\n",
    "    state_dict = state_dict[\"model_state\"]\n",
    "model.load_state_dict(state_dict, strict=True)\n",
    "\n",
    "if (hasattr(model, \"device\") and model.device != device) or not hasattr(\n",
    "    model, \"device\"\n",
    "):\n",
    "    model = model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference\n",
    "We are now ready to perform inference on the model. The data\n",
    "returned from the dataset class requires additional\n",
    "preprocessing; therefore, after polling the dataset, we process\n",
    "the data using the `preproc` function. This processed data is\n",
    "then transferred to the device. To recover the masking, we can\n",
    "save the torch RNG state and use it later. Finally, we run the\n",
    "model in evaluation mode without generating the gradient graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PrithviWxC.dataloaders.merra2 import preproc\n",
    "\n",
    "data = next(iter(dataset))\n",
    "batch = preproc([data], padding)\n",
    "\n",
    "for k, v in batch.items():\n",
    "    if isinstance(v, torch.Tensor):\n",
    "        batch[k] = v.to(device)\n",
    "\n",
    "rng_state_1 = torch.get_rng_state()\n",
    "with torch.no_grad():\n",
    "    model.eval()\n",
    "    out = model(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t2m = out[0, 12].cpu().numpy()\n",
    "\n",
    "lat = np.linspace(-90, 90, out.shape[-2])\n",
    "lon = np.linspace(-180, 180, out.shape[-1])\n",
    "X, Y = np.meshgrid(lon, lat)\n",
    "\n",
    "plt.contourf(X, Y, t2m, 100)\n",
    "plt.gca().set_aspect(\"equal\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
